137 citations · 212 across the 21 of their papers we have counts for
6 papers · 1 filter
The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics
Christoph Leiter, Juri Opitz, Daniel Deutsch +3
With an increasing number of parameters and pre-training data, generative large language models (LLMs) have shown remarkable capabilities to solve tasks with minimal or no task-rel…
Gzip versus bag-of-words for text classification
Juri Opitz
The effectiveness of compression in text classification ('gzip') has recently garnered lots of attention. In this note we show that `bag-of-words' approaches can achieve similar or…
AMR4NLI: Interpretable and robust NLI measures from semantic graphs
Juri Opitz, Shira Wein, Julius Steen +2
The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but t…
With a Little Push, NLI Models can Robustly and Efficiently Predict Faithfulness
Julius Steen, Juri Opitz, Anette Frank +1
Conditional language models still generate unfaithful output that is not supported by their input. These unfaithful generations jeopardize trust in real-world applications such as…
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks
Moritz Plenz, Juri Opitz, Philipp Heinisch +2
Arguments often do not make explicit how a conclusion follows from its premises. To compensate for this lack, we enrich arguments with structured background knowledge to support kn…
SMATCH++: Standardized and Extended Evaluation of Semantic Graphs
Juri Opitz
The Smatch metric is a popular method for evaluating graph distances, as is necessary, for instance, to assess the performance of semantic graph parsing systems. However, we observ…